Applied experiential learning is a core component of the DSI educational mission. The Data Science Clinic provides students with projects that transcend the traditional classroom experience. The Clinic is a project-based course where students work in teams as data scientists with real-world clients under the supervision of mentor instructors. To do this we partner with industry, social impact, civic organizations and research groups for 1 or 2 academic quarters on data science projects. As part of their training, students will be tasked with producing key deliverables, such as data analysis, open source software, as well as final client presentations and research reports.
Each project is assigned a team of 2 to 5 students and a mentor. Mentors are assigned to each project to ensure projects are structured with frequent milestones and generate tangible results. Teams consist of undergraduate juniors and seniors or masters students drawing largely from data science, computer science, public policy, statistics, economics and additional appropriate domains of study. Students work with real-world (imperfect) datasets, apply models and algorithms to data, navigate security and privacy issues, communicate results to a diverse set of stakeholders, and translate information into actionable insight. Each team of students meet weekly with one or more of the course leadership team in an extended scrum format to review the past week’s progress, address challenges and questions, reflect on overall team progress and update the timeline and goals of the weeks ahead. Each student works 10-15 hours a week on the Clinic project.
We work with our Clinic partners to scope projects, outline deliverables and place student teams onto these projects. Partners can expect the students to work diligently and take ownership of the project. Clinic partners provide a project appraisal that impacts finals grades.
As needed, mentors and students will sign a non-disclosure agreement at the company’s request and all IP generated by the project is retained by the Clinic partner. Participation in the Clinic is limited to organizations in our Industry Affiliates Program, our research collaborators, government and nonprofit organizations, and 11th Hour Project grantees.
For more information on the Data Science Clinic or our Industry Affiliates Program, contact us.
Data Science Clinic Staff
Nick RossData Science Clinic Director, Data Science Institute; Associate Senior Instructional Professor
Tim HannifanAssistant Clinic Director, Data Science Institute
Dr. Ross is an experienced data science executive and academic leader who specializes in leveraging business, engineering, and data to optimize decision-making. His various roles have ranged from architecting and designing production ML/AI systems, to hiring, growing, and leading engineering and data science teams.
Previously, Dr. Ross led the data science and backend engineering efforts at The Meta, an esports training platform used by millions of competitive gamers. Before joining The Meta, Dr. Ross was a Professor of Data Science at the University of San Francisco, where his research focused on how to effectively use data and data science techniques to answer business questions. During this time, he was also the Assistant Director of the University of San Francisco’s Data Institute, where he led and developed academic-industry partnerships to create a world-class masters of data science program. Under his leadership, the Data Institute placed hundreds of students into top data science positions in both the private and public sectors, with a job placement rate of over 90% within 3 months of graduation. As a consultant, he spearheaded data efforts at leading tech companies in the video and online game industry, from early-stage startups to multinational companies.
Dr. Ross received his PhD from UCLA, his Masters from UC Davis, and his Bachelor of Science from UC Berkeley. He has published papers in a variety of journals as well as given talks in both academic and industry settings.
Tim Hannifan is Assistant Clinic Director for the Data Science Institute at the University of Chicago. He is responsible for managing the execution of an experiential learning program for University students, recruiting and retaining data science partner organizations, and mentoring student teams to implement data science solutions for government, industry, and social impact organizations. Prior to joining the DSI, Tim worked on data science initiatives at Mathematica Policy Research and The World Bank. He holds a MS in Computational Analysis and Public Policy and a BA in Economics from the University of Chicago.